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The cytoskeletal architecture can be studied using different microscopic and biochemical techniques. Electron microscopy was instrumental in discovering the cytoskeletal architecture around the 1960s, which allowed obtaining structural information at a high-resolution level. However, the sample preparation procedure often limits this ability in biological samples. Several protocols have been developed over the years to optimize sample preparation. In one of the protocols known as rotary...

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cytoNet: Spatiotemporal network analysis of cell communities.

Arun S Mahadevan1,2, Byron L Long2,3,4, Chenyue W Hu2

  • 1Department of Bioengineering, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America.

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|June 13, 2022
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cytoNet is a new cloud-based tool that uses network science to analyze cell communities in microscopy images. It reveals how cell interactions influence individual cell behavior and community dynamics.

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Area of Science:

  • Cell biology
  • Bioinformatics
  • Network science

Background:

  • Understanding multicellular dynamics is crucial for many biological processes.
  • Current methods often lack the ability to quantify complex cell-cell interactions and their impact on cellular phenotypes.
  • Analyzing spatial topology and functional relationships within cell communities remains a challenge.

Purpose of the Study:

  • To introduce cytoNet, a novel cloud-based computational tool for characterizing cell populations from microscopy images.
  • To leverage network science principles for quantifying spatial topology and functional relationships in cell communities.
  • To evaluate the impact of cell-cell interactions on individual cell phenotypes and multicellular dynamics.

Main Methods:

  • cytoNet quantifies multicellular dynamics using graph features derived from microscopy images.
  • The tool analyzes spatial topology and functional relationships within cell communities.
  • It assesses the effect of cell-cell interactions on individual cell phenotypes.

Main Results:

  • Demonstrated cytoNet's utility in four distinct case studies.
  • Characterized temporal dynamics of neural progenitor cell communities during differentiation.
  • Identified pain-sensing neuron communities in vivo and analyzed their spatial organization.
  • Captured the influence of cell community on endothelial cell morphology.
  • Investigated the effect of laminin α4 on perivascular niches in adipose tissue.

Conclusions:

  • cytoNet provides a quantitative framework for studying complex cell communities and their dynamics.
  • The tool enhances understanding of environmental effects on cellular behavior.
  • Its cloud-based, versatile format makes advanced image analysis accessible to researchers across various scientific domains.